Predicting Dust Temperature from Molecular Line Data Using Machine Learning

Tenta Dougome

doi:10.3847/1538-4357/ae2ec3

2026-02-10

PRE

DOI：10.3847/1538-4357/ae2ec3

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摘要

En 中文

我们使用机器学习技术，从猎户座A分子云的CO同位素分子谱线数据中构建尘埃温度图。在传统的天体物理方法中，需要多波段连续谱数据来推导尘埃温度。本研究旨在探讨机器学习技术在无多波段尘埃连续谱数据区域推导尘埃温度的能力和局限性。我们研究了用于训练的像素数量如何影响预测精度，以及训练区域中采样的尘埃温度如何影响预测精度。我们发现，类似于观测区域总像素数5%的数量足以通过训练获得准确的预测。此外，训练区域中的尘埃温度样本应覆盖整个温度范围，并且具有与整个观测区域相似的样本分布，才能进行准确预测。12CO/13CO比值常被发现在预测尘埃温度时是最重要的特征。由于12CO/13CO比值是光子主导区域（PDRs）的示踪剂，机器学习技术能够将尘埃温度与PDRs联系起来。我们还发现，从分子谱线数据准确预测尘埃温度并不需要热气体-尘埃耦合条件，且机器学习能够捕捉比传统天体物理概念更多的信息。

Keyword:

FILAMENTARY STRUCTURE

CONTINUUM EMISSION

STAR-FORMATION

CLOUD COMPLEX

DENSE CORES

ORION

GAS

(CO)-C-13

期刊

Astrophysical Journal

IF:

5.4

论文数:

8.3W

被引数:

32.0W

机构

kagoshima university

学者数:

7.1K

论文数: 4.9K

被引数: 6

kyushu kyoritsu university

学者数:

论文数: 37

被引数: 0

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